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首页> 外文期刊>EURASIP journal on advances in signal processing >Low-Complexity Blind Symbol Timing Offset Estimation in OFDM Systems
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Low-Complexity Blind Symbol Timing Offset Estimation in OFDM Systems

机译:OFDM系统中的低复杂度盲符号定时偏移估计

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A low-complexity blind timing algorithm is proposed to estimate timing offset in OFDM systems when multiple symbols are received (the timing offset estimation is independent of the frequency offset one). Though the maximum-likelihood estimation (MLE) using two or three symbols is good in offset estimation, its performance can be significantly improved by including more symbols in our previous work. However, timing offset estimation requires exhaustive search and a priori knowledge of the probability distribution of the received data. The method we propose utilizes the second-order statistics embedded in a cyclic prefix. An information vector (IVR) with the same length as the cyclic prefix is formed based on an autocorrelation matrix (AM). The modulus of elements in the IVR is first quantized based on a threshold that is defined by the variance of OFDM symbols. The timing offset is then estimated based on the binary sequence of the IVR. Because the exhaustive search used in the MLE can be avoided, computational complexity is significantly reduced. In practice, the proposed scheme can be used as a coarse synchronization estimation that can rapidly provide a rough and contractible estimation range, which serves as the basis for a fine estimation like the MLE. The proposed estimator will be proved theoretically to be asymptotically unbiased and mean-squared consistent. Simulations and comparisons will be provided in the paper to illustrate the advantages of our design.
机译:提出了一种低复杂度盲定时算法,用于在接收到多个符号时估计OFDM系统中的定时偏移(定时偏移估计与频率偏移1无关)。尽管使用两个或三个符号的最大似然估计(MLE)在偏移估计中比较好,但通过在我们以前的工作中包含更多符号,可以显着提高其性能。然而,定时偏移估计需要详尽的搜索和对接收数据的概率分布的先验知识。我们提出的方法利用嵌入在循环前缀中的二阶统计量。基于自相关矩阵(AM),形成长度与循环前缀相同的信息矢量(IVR)。首先基于由OFDM符号的方差定义的阈值来量化IVR中的元素的模数。然后基于IVR的二进制序列来估计定时偏移。因为可以避免在MLE中使用穷举搜索,所以大大降低了计算复杂度。在实践中,所提出的方案可以用作粗同步估计,其可以快速提供粗略和可收缩的估计范围,其用作像MLE这样的精细估计的基础。理论上将证明所提出的估计量是渐近无偏的,并且均方一致。本文将提供仿真和比较,以说明我们设计的优势。

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